DonorsChoose¶
DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.
Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:
- How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
- How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
- How to focus volunteer time on the applications that need the most assistance
The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.
About the DonorsChoose Data Set¶
The train.csv data set provided by DonorsChoose contains the following features:
| Feature | Description |
|---|---|
project_id |
A unique identifier for the proposed project. Example: p036502 |
project_title |
Title of the project. Examples:
|
project_grade_category |
Grade level of students for which the project is targeted. One of the following enumerated values:
|
project_subject_categories |
One or more (comma-separated) subject categories for the project from the following enumerated list of values:
Examples:
|
school_state |
State where school is located (Two-letter U.S. postal code). Example: WY |
project_subject_subcategories |
One or more (comma-separated) subject subcategories for the project. Examples:
|
project_resource_summary |
An explanation of the resources needed for the project. Example:
|
project_essay_1 |
First application essay* |
project_essay_2 |
Second application essay* |
project_essay_3 |
Third application essay* |
project_essay_4 |
Fourth application essay* |
project_submitted_datetime |
Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245 |
teacher_id |
A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56 |
teacher_prefix |
Teacher's title. One of the following enumerated values:
|
teacher_number_of_previously_posted_projects |
Number of project applications previously submitted by the same teacher. Example: 2 |
* See the section Notes on the Essay Data for more details about these features.
Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:
| Feature | Description |
|---|---|
id |
A project_id value from the train.csv file. Example: p036502 |
description |
Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25 |
quantity |
Quantity of the resource required. Example: 3 |
price |
Price of the resource required. Example: 9.95 |
Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:
The data set contains the following label (the value you will attempt to predict):
| Label | Description |
|---|---|
project_is_approved |
A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved. |
Notes on the Essay Data¶
-
Prior to May 17, 2016, the prompts for the essays were as follows:
- __project_essay_1:__ "Introduce us to your classroom"
- __project_essay_2:__ "Tell us more about your students"
- __project_essay_3:__ "Describe how your students will use the materials you're requesting"
- __project_essay_3:__ "Close by sharing why your project will make a difference"
-
Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
- __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
- __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"
For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.
In [1]:
import os
os.chdir('F:/f/MY____/AAIC/AssignmentS/DonorsChoose_Data')
os.getcwd()
Out[1]:
In [33]:
from tqdm import tqdm
for i in tqdm(range(int(10e6))):
pass
In [3]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sea
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import roc_auc_score
from nltk.stem.porter import PorterStemmer
import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle
from tqdm import tqdm
import os
from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter
1.1 Reading Data¶
In [4]:
project_data = pd.read_csv('train_data.csv', nrows=50000)
resource_data = pd.read_csv('resources.csv')
In [5]:
print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)
print(type(project_data))
print(type(resource_data))
In [7]:
print("Number of data Points in Resources Data ",resource_data.shape)
print("-"*50)
print("The Features of resources data ", resource_data.columns.values)
In [8]:
print("Number of data points in resource data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)
Out[8]:
In [ ]:
1.2 preprocessing of project_subject_categories¶
In [9]:
categories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in categories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_') # we are replacing the & value into
cat_list.append(temp.strip())
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)
from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
my_counter.update(word.split())
cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))
1.3 preprocessing of project_subject_subcategories¶
In [10]:
sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
sub_cat_list = []
for i in sub_catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_')
sub_cat_list.append(temp.strip())
project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
my_counter.update(word.split())
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))
1.3 Text preprocessing¶
In [11]:
# merge two column text dataframe:
project_data["essay"] = project_data["project_essay_1"].map(str) +\
project_data["project_essay_2"].map(str) + \
project_data["project_essay_3"].map(str) + \
project_data["project_essay_4"].map(str)
In [12]:
project_data.head(2)
Out[12]:
In [11]:
#### 1.4.2.3 Using Pretrained Models: TFIDF weighted W2V
In [13]:
# printing some random reviews
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
#print(project_data['essay'].values[99999])
print("="*50)
In [14]:
# https://stackoverflow.com/a/47091490/4084039
import re
def decontracted(phrase):
# specific
phrase = re.sub(r"won't", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
In [15]:
sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)
In [16]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)
In [17]:
#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)
In [18]:
# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
"you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
"hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
"mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
'won', "won't", 'wouldn', "wouldn't"]
In [18]:
# Combining all the above stundents
#from tqdm import tqdm
#preprocessed_essays = []
## tqdm is for printing the status bar
#for sentance in tqdm(project_data['essay'].values):
# sent = decontracted(sentance)
# sent = sent.replace('\\r', ' ')
# sent = sent.replace('\\"', ' ')
# sent = sent.replace('\\n', ' ')
# sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# # https://gist.github.com/sebleier/554280
# sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
# preprocessed_essays.append(sent.lower().strip())
In [19]:
# after preprocesing
#preprocessed_essays[20000]
1.4 Preprocessing of `project_title`
In [ ]:
#sent = decontracted(project_data['project_title'].values[12])
#print(sent)
In [ ]:
# similarly you can preprocess the titles also
#from tqdm import tqdm
#preprocessed_titles = []
## tqdm is for printing the status bar
#for sentance in tqdm(project_data['project_title'].values):
# sent = decontracted(sentance)
# sent = sent.replace('\\r', ' ')
# sent = sent.replace('\\"', ' ')
# sent = sent.replace('\\n', ' ')
# sent = re.sub('[b^A-Za-z0-9]+', ' ', sent)
# # https://gist.github.com/sebleier/554280
# sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
# preprocessed_titles.append(sent.lower().strip())
In [ ]:
1.5 Preparing data for models¶
Train_Test_Split¶
In [19]:
#Splitting Data into train and Test sklearn https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(project_data,
project_data["project_is_approved"],
test_size = 0.33, stratify = project_data["project_is_approved"], random_state = 42)
In [20]:
x_train, x_cv, y_train, y_cv = train_test_split(x_train, y_train,
test_size = 0.33, stratify = y_train, random_state = 42)
In [21]:
print(x_test.columns)
print(x_train.columns)
#print(x_cv.columns)
#print(x_train.shape)
#print(x_test.shape)
#print(x_cv.shape)
In [22]:
#https://stackoverflow.com/questions/13411544/delete-column-from-pandas-dataframe-by-column-name
#x_train =
x_train.drop(["project_is_approved"], axis = 1, inplace = True)
#x_test =
x_test.drop(["project_is_approved"], axis = 1, inplace = True)
#x_cv =
x_cv.drop(["project_is_approved"], axis = 1, inplace = True)
In [23]:
print(x_train.columns)
In [24]:
print(x_train['essay'].values[0])
In [25]:
sent = decontracted(x_train['essay'].values[2000])
print(sent)
print("="*50)
Preprocessing Train Data¶
In [26]:
# Preprocessing Train Data of Project Essays
from tqdm import tqdm
train_preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(x_train['essay'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
train_preprocessed_essays.append(sent.lower().strip())
In [27]:
train_preprocessed_essays[10]
Out[27]:
In [28]:
#Preprocessing Test Data of Project Essays
# Combining all the above stundents
from tqdm import tqdm
test_preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(x_test['essay'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
test_preprocessed_essays.append(sent.lower().strip())
In [34]:
test_preprocessed_essays[10]
Out[34]:
In [35]:
#Preprocessing Cross Validation Data of Project Essays
# Combining all the above stundents
from tqdm import tqdm
cv_preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(x_cv['essay'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
cv_preprocessed_essays.append(sent.lower().strip())
In [36]:
cv_preprocessed_essays[10]
Out[36]:
Preprocessing Project Titles¶
In [37]:
#Preprocessing Train Data for Project Titles
from tqdm import tqdm
train_preprocessed_titles = []
# tqdm is for printing the status bar
for sentance in tqdm(x_train['project_title'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
train_preprocessed_titles.append(sent.lower().strip())
In [38]:
train_preprocessed_titles[10]
Out[38]:
In [39]:
#Preprocessing Test Data for Project Titles
from tqdm import tqdm
test_preprocessed_titles = []
# tqdm is for printing the status bar
for sentance in tqdm(x_test['project_title'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
test_preprocessed_titles.append(sent.lower().strip())
In [40]:
test_preprocessed_titles[10]
Out[40]:
In [41]:
#Preprocessing CV Data for Project Titles
from tqdm import tqdm
cv_preprocessed_titles = []
# tqdm is for printing the status bar
for sentance in tqdm(x_cv['project_title'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
cv_preprocessed_titles.append(sent.lower().strip())
In [42]:
cv_preprocessed_titles[10]
Out[42]:
In [43]:
project_data.columns
Out[43]:
we are going to consider
- school_state : categorical data
- clean_categories : categorical data
- clean_subcategories : categorical data
- project_grade_category : categorical data
- teacher_prefix : categorical data
- project_title : text data
- text : text data
- project_resource_summary: text data (optinal)
- quantity : numerical (optinal)
- teacher_number_of_previously_posted_projects : numerical
- price : numerical1.5.1 Vectorizing Categorical data¶
In [44]:
# we use count vectorizer to convert the values into one
# Vectorizing Clean Categories
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(x_train['clean_categories'].values)
train_categories_one_hot = vectorizer.transform(x_train['clean_categories'].values)
test_categories_one_hot = vectorizer.transform(x_test['clean_categories'].values)
cv_categories_one_hot = vectorizer.transform(x_cv['clean_categories'].values)
print(vectorizer.get_feature_names())
print("Shape of Train matrix after one hot encodig ",train_categories_one_hot.shape)
print("Shape of Test matrix after one hot encodig ",test_categories_one_hot.shape)
print("Shape of cv matrix after one hot encodig ",cv_categories_one_hot.shape)
In [45]:
# we use count vectorizer to convert the values into one
vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(x_train["clean_subcategories"].values)
train_sub_categories_one_hot = vectorizer.transform(x_train['clean_subcategories'].values)
test_sub_categories_one_hot = vectorizer.transform(x_test['clean_subcategories'].values)
cv_sub_categories_one_hot = vectorizer.transform(x_cv['clean_subcategories'].values)
print(vectorizer.get_feature_names())
print("Shape of Train matrix after Trainone hot encodig ",train_sub_categories_one_hot.shape)
print("Shape of test matrix after one hot encodig ",test_sub_categories_one_hot.shape)
print("Shape of cv_ matrix after one hot encodig ",cv_sub_categories_one_hot.shape)
In [46]:
# you can do the similar thing with state, teacher_prefix and project_grade_category also
from collections import Counter
my_counter = Counter()
for word in project_data["school_state"].values:
my_counter.update(word.split())
In [47]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
state_cat_dict = dict(my_counter)
storted_state_cat_dict = dict(sorted(state_cat_dict.items(), key=lambda kv: kv[1]))
In [48]:
# Please do the similar feature encoding with state, teacher_prefix and project_grade_category also
#Using Count Vectorizer to convert the state value onto on hot encoded feature
vectorizer = CountVectorizer(vocabulary=list(storted_state_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['school_state'].values)
print(vectorizer.get_feature_names())
train_state_one_hot = vectorizer.transform(x_train['school_state'].values)
test_state_one_hot = vectorizer.transform(x_test['school_state'].values)
cv_state_one_hot = vectorizer.transform(x_cv['school_state'].values)
print("Shape of Train matrix after one hot encodig ",train_state_one_hot.shape)
print("Shape of Test matrix after one hot encodig ",test_state_one_hot.shape)
print("Shape of CV matrix after one hot encodig ",cv_state_one_hot.shape)
In [49]:
#https://stackoverflow.com/questions/42224700/attributeerror-float-object-has-no-attribute-split
project_data['project_grade_category']=project_data['project_grade_category'].fillna("")
my_counter = Counter()
for word in project_data['project_grade_category'].values:
my_counter.update(word.split())
In [50]:
project_cat_dict = dict(my_counter)
sorted_project_cat_dict = dict(sorted(project_cat_dict.items(), key=lambda kv: kv[1]))
In [51]:
# feature encoding for project_grade_category also
vectorizer = CountVectorizer(vocabulary=list(sorted_project_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['project_grade_category'].values)
print(vectorizer.get_feature_names())
train_grade_one_hot = vectorizer.transform(x_train['project_grade_category'].values)
test_grade_one_hot = vectorizer.transform(x_test['project_grade_category'].values)
cv_grade_one_hot = vectorizer.transform(x_cv['project_grade_category'].values)
print("Shape of Train matrix after one hot encodig ",train_grade_one_hot.shape)
print("Shape of test matrix after one hot encodig ",test_grade_one_hot.shape)
print("Shape of cv matrix after one hot encodig ",cv_grade_one_hot.shape)
In [52]:
#https://stackoverflow.com/questions/42224700/attributeerror-float-object-has-no-attribute-split
project_data['teacher_prefix']=project_data['teacher_prefix'].fillna(" ")
In [53]:
my_counter = Counter()
for word in project_data['teacher_prefix'].values:
my_counter.update(word.split())
In [54]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
teacher_cat_dict = dict(my_counter)
sorted_teacher_cat_dict = dict(sorted(teacher_cat_dict.items(), key=lambda kv: kv[1]))
In [55]:
#Using Count Vectorizer to convert the teacher_prefix value onto on hot encoded feature
#ValueError: np.nan is an invalid document, expected byte or unicode string.
#https://stackoverflow.com/questions/39303912/tfidfvectorizer-in-scikit-learn-valueerror-np-nan-is-an-invalid-document
vectorizer = CountVectorizer(vocabulary=list(sorted_teacher_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['teacher_prefix'].values.astype('U'))
print(vectorizer.get_feature_names())
train_teacher_one_hot = vectorizer.transform(x_train['teacher_prefix'].values.astype('U'))
test_teacher_one_hot = vectorizer.transform(x_test['teacher_prefix'].values.astype('U'))
cv_teacher_one_hot = vectorizer.transform(x_cv['teacher_prefix'].values.astype('U'))
print("Shape of Train matrix after one hot encodig ",train_teacher_one_hot.shape)
print("Shape of Test matrix after one hot encodig ",test_teacher_one_hot.shape)
print("Shape of CV matrix after one hot encodig ",cv_teacher_one_hot.shape)
1.5.2 Vectorizing Text data¶
1.5.2.1 Bag of words Project Essays¶
In [56]:
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
vectorizer.fit(train_preprocessed_essays)
train_text_bow = vectorizer.transform(train_preprocessed_essays)
print("Shape of matrix after one hot encodig ",train_text_bow.shape)
In [57]:
# you can vectorize the title also
# before you vectorize the title make sure you preprocess it
test_text_bow = vectorizer.transform(test_preprocessed_essays)
print("Shape of matrix after one hot encodig ",test_text_bow.shape)
In [58]:
# Vectrozing Cv
cv_text_bow = vectorizer.transform(cv_preprocessed_essays)
print("Shape of matrix after one hot encodig ",cv_text_bow.shape)
Project Titles¶
In [59]:
vectorizer.fit(train_preprocessed_titles)
train_title_bow = vectorizer.fit_transform(train_preprocessed_titles)
print("Shape of matrix after one hot encodig ",train_title_bow.shape)
In [60]:
test_title_bow = vectorizer.transform(test_preprocessed_titles)
print("Shape of matrix after one hot encodig ",test_title_bow.shape)
In [61]:
cv_title_bow = vectorizer.transform(cv_preprocessed_titles)
print("Shape of matrix after one hot encodig ",cv_title_bow.shape)
1.5.2.2 TFIDF vectorizer Project Essays¶
In [62]:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
vectorizer.fit(train_preprocessed_essays)
train_text_tfidf = vectorizer.transform(train_preprocessed_essays)
print("Shape of matrix after one hot encodig ",train_text_tfidf.shape)
In [63]:
test_text_tfidf = vectorizer.transform(test_preprocessed_essays)
print("Shape of matrix after one hot encodig ",test_text_tfidf.shape)
In [64]:
cv_text_tfidf = vectorizer.transform(cv_preprocessed_essays)
print("Shape of matrix after one hot encodig ",cv_text_tfidf.shape)
Project Titles¶
In [65]:
vectorizer = TfidfVectorizer(min_df=10)
vectorizer.fit(train_preprocessed_titles)
train_title_tfidf = vectorizer.transform(train_preprocessed_titles)
print("Shape of matrix after one hot encodig ",train_title_tfidf.shape)
In [66]:
test_title_tfidf = vectorizer.transform(test_preprocessed_titles)
print("Shape of matrix after one hot encodig ",test_title_tfidf.shape)
In [67]:
cv_title_tfidf = vectorizer.transform(cv_preprocessed_titles)
print("Shape of matrix after one hot encodig ",cv_title_tfidf.shape)
1.5.2.3 Using Pretrained Models: Avg W2V¶
In [68]:
'''
# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
def loadGloveModel(gloveFile):
print ("Loading Glove Model")
f = open(gloveFile,'r', encoding="utf8")
model = {}
for line in tqdm(f):
splitLine = line.split()
word = splitLine[0]
embedding = np.array([float(val) for val in splitLine[1:]])
model[word] = embedding
print ("Done.",len(model)," words loaded!")
return model
model = loadGloveModel('glove.42B.300d.txt')
# ============================
Output:
Loading Glove Model
1917495it [06:32, 4879.69it/s]
Done. 1917495 words loaded!
# ============================
words = []
for i in preproced_texts:
words.extend(i.split(' '))
for i in preproced_titles:
words.extend(i.split(' '))
print("all the words in the coupus", len(words))
words = set(words)
print("the unique words in the coupus", len(words))
inter_words = set(model.keys()).intersection(words)
print("The number of words that are present in both glove vectors and our coupus", \
len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")
words_courpus = {}
words_glove = set(model.keys())
for i in words:
if i in words_glove:
words_courpus[i] = model[i]
print("word 2 vec length", len(words_courpus))
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
import pickle
with open('glove_vectors', 'wb') as f:
pickle.dump(words_courpus, f)
'''
Out[68]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [69]:
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
with open('glove_vectors', 'rb') as f:
model = pickle.load(f)
glove_words = set(model.keys())
In [70]:
# average Word2Vec
# compute average word2vec for each review.
train_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(train_preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
train_avg_w2v_vectors.append(vector)
print(len(train_avg_w2v_vectors))
print(len(train_avg_w2v_vectors[0]))
In [71]:
# average Word2Vec
# compute average word2vec for each review.
test_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(test_preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
test_avg_w2v_vectors.append(vector)
print(len(test_avg_w2v_vectors))
print(len(test_avg_w2v_vectors[0]))
In [72]:
# average Word2Vec
# compute average word2vec for each review.
cv_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(cv_preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
cv_avg_w2v_vectors.append(vector)
print(len(cv_avg_w2v_vectors))
print(len(cv_avg_w2v_vectors[0]))
Projects Titles¶
In [73]:
# average Word2Vec
# compute average word2vec for each review.
train_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(train_preprocessed_titles): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
train_title_avg_w2v_vectors.append(vector)
print(len(train_title_avg_w2v_vectors))
print(len(train_title_avg_w2v_vectors[0]))
In [74]:
# average Word2Vec
# compute average word2vec for each review.
test_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(test_preprocessed_titles): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
test_title_avg_w2v_vectors.append(vector)
print(len(test_title_avg_w2v_vectors))
print(len(test_title_avg_w2v_vectors[0]))
In [75]:
# average Word2Vec
# compute average word2vec for each review.
cv_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(cv_preprocessed_titles): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
cv_title_avg_w2v_vectors.append(vector)
print(len(cv_title_avg_w2v_vectors))
print(len(cv_title_avg_w2v_vectors[0]))
1.5.2.3 Using Pretrained Models: TFIDF weighted W2V¶
In [76]:
# S = ["abc def pqr", "def def def abc", "pqr pqr def"]
tfidf_model = TfidfVectorizer()
tfidf_model.fit(train_preprocessed_essays)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [77]:
# average Word2Vec
# compute average word2vec for each review.
train_essay_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(train_preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
train_essay_tfidf_w2v_vectors.append(vector)
print(len(train_essay_tfidf_w2v_vectors))
print(len(train_essay_tfidf_w2v_vectors[0]))
In [78]:
# Similarly you can vectorize for title also
# average Word2Vec
# compute average word2vec for each review.
test_essay_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(test_preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
test_essay_tfidf_w2v_vectors.append(vector)
print(len(test_essay_tfidf_w2v_vectors))
print(len(test_essay_tfidf_w2v_vectors[0]))
In [79]:
# average Word2Vec
# compute average word2vec for each review.
cv_essay_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(cv_preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
cv_essay_tfidf_w2v_vectors.append(vector)
print(len(cv_essay_tfidf_w2v_vectors))
print(len(cv_essay_tfidf_w2v_vectors[0]))
Project Titles¶
In [80]:
# average Word2Vec
# compute average word2vec for each review.
train_title_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(train_preprocessed_titles): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
train_title_tfidf_w2v_vectors.append(vector)
print(len(train_title_tfidf_w2v_vectors))
print(len(train_title_tfidf_w2v_vectors[0]))
In [81]:
# average Word2Vec
# compute average word2vec for each review.
test_title_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(test_preprocessed_titles): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
test_title_tfidf_w2v_vectors.append(vector)
print(len(test_title_tfidf_w2v_vectors))
print(len(test_title_tfidf_w2v_vectors[0]))
In [82]:
# average Word2Vec
# compute average word2vec for each review.
cv_title_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(cv_preprocessed_titles): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
cv_title_tfidf_w2v_vectors.append(vector)
print(len(cv_title_tfidf_w2v_vectors))
print(len(cv_title_tfidf_w2v_vectors[0]))
1.5.3 Vectorizing Numerical features¶
In [83]:
price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
project_data = pd.merge(project_data, price_data, on='id', how='left')
print(price_data.head())
#print(project_data.columns)
print(x_train.columns)
In [84]:
# - quantity : numerical (optinal)
# - teacher_number_of_previously_posted_projects : numerical
# - price : numerical
x_train = pd.merge(x_train, price_data, on = "id", how = "left")
#print(x_train.columns)
x_test = pd.merge(x_test, price_data, on = "id", how = "left")
x_cv = pd.merge(x_cv, price_data, on = "id", how = "left")
Standardize Price¶
In [85]:
#ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
#https://stackoverflow.com/questions/37716699/how-to-hstack-several-sparse-matrices-feature-matrices
#https://datascience.stackexchange.com/questions/11928/valueerror-input-contains-nan-infinity-or-a-value-too-large-for-dtypefloat32
#x_train = x_train['price'].as_matrix().astype(np.float)
#x_train = x_train.fillna(x_train.mean())
In [86]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler
# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329. ... 399. 287.73 5.5 ].
# Reshape your data either using array.reshape(-1, 1)
price_scalar = StandardScaler()
price_scalar.fit(x_train['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_price_standar = price_scalar.transform(x_train['price'].values.reshape(-1, 1))
train_price_standar
Out[86]:
In [87]:
price_scalar.fit(x_test['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_price_standar = price_scalar.transform(x_test['price'].values.reshape(-1, 1))
test_price_standar
Out[87]:
In [88]:
price_scalar.fit(x_cv['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
cv_price_standar = price_scalar.transform(x_cv['price'].values.reshape(-1, 1))
test_price_standar
Out[88]:
In [89]:
print(train_price_standar.shape, y_train.shape)
print(test_price_standar.shape, y_test.shape)
print(cv_price_standar.shape, y_cv.shape)
Standardize Teacher previously posted Projects¶
In [90]:
warnings.filterwarnings("ignore")
price_scalar.fit(x_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_prev_proj_standar = price_scalar.transform(x_train['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))
train_prev_proj_standar
Out[90]:
In [91]:
price_scalar.fit(x_test['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_prev_proj_standar = price_scalar.transform(x_test['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))
test_prev_proj_standar
Out[91]:
In [92]:
price_scalar.fit(x_cv['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
cv_prev_proj_standar = price_scalar.transform(x_cv['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))
cv_prev_proj_standar
Out[92]:
In [93]:
print(train_prev_proj_standar.shape, y_train.shape)
print(test_prev_proj_standar.shape, y_test.shape)
print(cv_prev_proj_standar.shape, y_cv.shape)
Standardize Quantity¶
In [94]:
price_scalar.fit(x_train['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_quantity_standar = price_scalar.transform(x_train['quantity'].values.reshape(-1, 1))
train_quantity_standar
Out[94]:
In [95]:
price_scalar.fit(x_test['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_quantity_standar = price_scalar.transform(x_test['quantity'].values.reshape(-1, 1))
test_quantity_standar
Out[95]:
In [96]:
price_scalar.fit(x_cv['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
cv_quantity_standar = price_scalar.transform(x_cv['quantity'].values.reshape(-1, 1))
cv_quantity_standar
Out[96]:
In [97]:
print(train_quantity_standar.shape, y_train.shape)
print(test_quantity_standar.shape, y_test.shape)
print(cv_quantity_standar.shape, y_cv.shape)
1.5.4 Merging all the above features¶
- we need to merge all the numerical vectors i.e catogorical, text, numerical vectors
In [98]:
#Categorical
print("Shape of Train matrix after Trainone hot encodig ",train_categories_one_hot.shape)
print("Shape of test matrix after one hot encodig ",test_categories_one_hot.shape)
print("Shape of cv_ matrix after one hot encodig ",cv_categories_one_hot.shape)
In [99]:
#subcategorical
print("Shape of Train matrix after Trainone hot encodig ",train_sub_categories_one_hot.shape)
print("Shape of test matrix after one hot encodig ",test_sub_categories_one_hot.shape)
print("Shape of cv_ matrix after one hot encodig ",cv_sub_categories_one_hot.shape)
In [100]:
#State
print("Shape of Train matrix after one hot encodig ",train_state_one_hot.shape)
print("Shape of Test matrix after one hot encodig ",test_state_one_hot.shape)
print("Shape of CV matrix after one hot encodig ",cv_state_one_hot.shape)
In [101]:
#Grade
print("Shape of Train matrix after one hot encodig ",train_grade_one_hot.shape)
print("Shape of test matrix after one hot encodig ",test_grade_one_hot.shape)
print("Shape of cv matrix after one hot encodig ",cv_grade_one_hot.shape)
In [102]:
#Teacher
print("Shape of Train matrix after one hot encodig ",train_teacher_one_hot.shape)
print("Shape of Test matrix after one hot encodig ",test_teacher_one_hot.shape)
print("Shape of CV matrix after one hot encodig ",cv_teacher_one_hot.shape)
In [103]:
#BOW Project Essays
print("Shape of train matrix after one hot encodig ",train_text_bow.shape)
print("Shape of test matrix after one hot encodig ",test_text_bow.shape)
print("Shape of cv matrix after one hot encodig ",cv_text_bow.shape)
print("- "*50)
#BOW Project Titles
print("Shape of train matrix after one hot encodig ",train_title_bow.shape)
print("Shape of test matrix after one hot encodig ",test_title_bow.shape)
print("Shape of cv matrix after one hot encodig ",cv_title_bow.shape)
In [104]:
#TFIDF Project Essays
print("Shape of train matrix after one hot encodig ",train_text_tfidf.shape)
print("Shape of test matrix after one hot encodig ",test_text_tfidf.shape)
print("Shape of cv matrix after one hot encodig ",cv_text_tfidf.shape)
print("- "*50)
#TFIDF Project Title
print("Shape of train matrix after one hot encodig ",train_title_tfidf.shape)
print("Shape of matrix after one hot encodig ",test_title_tfidf.shape)
print("Shape of matrix after one hot encodig ",cv_title_tfidf.shape)
In [ ]:
In [ ]:
In [105]:
from scipy.sparse import hstack
In [106]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
#TypeError: 'coo_matrix' object is not subscriptable https://stackoverflow.com/questions/30163830/accessing-elements-in-coo-matrix
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X_train1 = hstack((train_categories_one_hot,train_sub_categories_one_hot,train_state_one_hot,train_grade_one_hot,
train_teacher_one_hot,train_text_bow, train_title_bow, train_quantity_standar,
train_prev_proj_standar, train_price_standar)).tocsr()
print(X_train1.shape, y_train.shape)
print(type(X_train1))
In [107]:
#X_trainn = X_train.todense() #NOT Working
In [108]:
X_test1 = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
test_teacher_one_hot, test_text_bow, test_title_bow, test_quantity_standar,
test_prev_proj_standar, test_price_standar)).tocsr()
print(X_test1.shape, y_test.shape)
print(type(X_test1))
In [109]:
X_cv1 = hstack((cv_categories_one_hot,cv_sub_categories_one_hot,cv_state_one_hot,cv_grade_one_hot,
cv_teacher_one_hot, cv_text_bow, cv_title_bow, cv_quantity_standar,
cv_prev_proj_standar, cv_price_standar)).tocsr()
print(X_cv1.shape, y_cv.shape)
print(type(X_cv1))
In [110]:
#from sklearn.neighbors import KNeighborsClassifier
#knn = KNeighborsClassifier(n_neighbors=50)
#knn.fit(X_train, y_train)
#pred1 = knn.predict(X_test)
In [111]:
#pred = knn.predict(X_test)
#accuracy_score(y_test, pred)
Assignment 3: Apply KNN¶
- [Task-1] Apply KNN(brute force version) on these feature sets
- Set 1: categorical, numerical features + project_title(BOW) + preprocessed_essay (BOW)
- Set 2: categorical, numerical features + project_title(TFIDF)+ preprocessed_essay (TFIDF)
- Set 3: categorical, numerical features + project_title(AVG W2V)+ preprocessed_essay (AVG W2V)
- Set 4: categorical, numerical features + project_title(TFIDF W2V)+ preprocessed_essay (TFIDF W2V)
- Hyper paramter tuning to find best K
- Find the best hyper parameter which results in the maximum AUC value
- Find the best hyper paramter using k-fold cross validation (or) simple cross validation data
- Use gridsearch-cv or randomsearch-cv or write your own for loops to do this task
-
Representation of results
- You need to plot the performance of model both on train data and cross validation data for each hyper parameter, as shown in the figure
- Once you find the best hyper parameter, you need to train your model-M using the best hyper-param. Now, find the AUC on test data and plot the ROC curve on both train and test using model-M.
- Along with plotting ROC curve, you need to print the confusion matrix with predicted and original labels of test data points
- You need to plot the performance of model both on train data and cross validation data for each hyper parameter, as shown in the figure
- [Task-2]
- Select top 2000 features from feature Set 2 using `SelectKBest` and then apply KNN on top of these features
-
from sklearn.datasets import load_digits from sklearn.feature_selection import SelectKBest, chi2 X, y = load_digits(return_X_y=True) X.shape X_new = SelectKBest(chi2, k=20).fit_transform(X, y) X_new.shape ======== output: (1797, 64) (1797, 20) - Repeat the steps 2 and 3 on the data matrix after feature selection
- Conclusion
- You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link
- You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link
Note: Data Leakage
- There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.
- To avoid the issue of data-leakag, make sure to split your data first and then vectorize it.
- While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.
- For more details please go through this link.
2.4 Appling KNN on different kind of featurization as mentioned in the instructions
Apply KNN on different kind of featurization as mentioned in the instructions
For Every model that you work on make sure you do the step 2 and step 3 of instructions
2.4.1 Applying KNN brute force on BOW_SET-1¶
In [ ]:
#from sklearn.neighbors import KNeighborsClassifier
#knn = KNeighborsClassifier(n_neighbors=50)
#knn.fit(X_train, y_train)
#pred1 = knn.predict(X_test)
In [162]:
print(X_train1.shape, y_train.shape)
print(X_test1.shape, y_test.shape)
print(X_cv1.shape, y_cv.shape)
In [113]:
#myList = list(range(1,50))
# subsetting just the odd ones
#neighbors = filter(lambda x: x % 2 != 0, myList)
# empty list that will hold cv scores
#cv_scores = []
# perform 10-fold cross validation
#for k in neighbors:
# knn = KNeighborsClassifier(n_neighbors=k)
# scores = cross_val_score(knn, X_train, y_train, cv=10, scoring='accuracy')#
# cv_scores.append(scores.mean())
In [ ]:
In [114]:
def batch_predict(clf, data):
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_data_pred = []
tr_loop = data.shape[0] - data.shape[0]%1000
# consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
# in this for loop we will iterate unti the last 1000 multiplier
for i in range(0, tr_loop, 1000):
y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
# we will be predicting for the last data points
y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
return y_data_pred
In [115]:
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import roc_auc_score
"""
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators.
y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of
decisions (as returned by “decision_function” on some classifiers).
For binary y_true, y_score is supposed to be the score of the class with greater label.
"""
train_auc = []
cv_auc = []
K = [1, 5, 10, 15, 21, 31, 41, 51, 101]
for i in tqdm(K):
neigh = KNeighborsClassifier(n_neighbors=i)
neigh.fit(X_train1, y_train)
y_train_pred = batch_predict(neigh, X_train1)
y_cv_pred = batch_predict(neigh, X_cv1)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
train_auc.append(roc_auc_score(y_train,y_train_pred))
cv_auc.append(roc_auc_score(y_cv, y_cv_pred))
plt.plot(K, train_auc, label='Train AUC')
plt.plot(K, cv_auc, label='CV AUC')
plt.scatter(K, train_auc, label='Train AUC points')
plt.scatter(K, cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
Grid Search¶
In [137]:
from sklearn.model_selection import GridSearchCV
In [ ]:
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
#from sklearn.model_selection import GridSearchCV
#neigh = KNeighborsClassifier()
#parameters = {'n_neighbors':[1, 11, 21, 31, 41, 51, 61, 71, 81, 91, 107, 121]}
#clf = GridSearchCV(neigh, parameters, cv= 5, scoring='roc_auc')
#clf.fit(X_train1, y_train)
#train_auc= clf.cv_results_['mean_train_score']
#train_auc_std= clf.cv_results_['std_train_score']
#cv_auc = clf.cv_results_['mean_test_score']
#cv_auc_std= clf.cv_results_['std_test_score']
#plt.plot(parameters['n_neighbors'], train_auc, label='Train AUC')
## this code is copied from here: https://stackoverflow.com/a/48803361/4084039
#plt.gca().fill_between(parameters['n_neighbors'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.3,color='darkblue')
#plt.plot(parameters['n_neighbors'], cv_auc, label='CV AUC')
## this code is copied from here: https://stackoverflow.com/a/48803361/4084039
#plt.gca().fill_between(parameters['n_neighbors'],cv_auc - cv_auc_std,cv_auc + cv_auc_std,alpha=0.3,color='darkorange')
#plt.scatter(parameters['n_neighbors'], train_auc, label='Train AUC points')
#plt.scatter(parameters['n_neighbors'], cv_auc, label='CV AUC points')
#plt.legend()
#plt.xlabel("K: hyperparameter")
#plt.ylabel("AUC")
#plt.title("ERROR PLOTS")
#plt.grid()
#plt.show()
Fitting Model to Hyper-Parameter Curve¶
TO Reduce the noise in the Pridcting Values Here we are Picking the Highest k Values
In [116]:
best_k_1 = 97
print(X_test1.shape)
In [117]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
neigh = KNeighborsClassifier(n_neighbors=best_k_1)
neigh.fit(X_train1, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(neigh, X_train1)
y_test_pred = batch_predict(neigh, X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
In [ ]:
In [118]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
t = threshould[np.argmax(fpr*(1-tpr))]
# (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
predictions = []
for i in proba:
if i>=t:
predictions.append(1)
else:
predictions.append(0)
return predictions
Confusion Matrix¶
In [120]:
print("="*100)
from sklearn.metrics import confusion_matrix
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
In [130]:
#How to print confusion matrix using Heat map
#https://stackoverflow.com/questions/35572000/how-can-i-plot-a-confusion-matrix
#https://tatwan.github.io/How-To-Plot-A-Confusion-Matrix-In-Python/
train_confusion_matrix = pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred, tr_thresholds,
train_fpr,train_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(train_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Train Confusion Matix")
Out[130]:
Test Data¶
In [1]:
#This is Future Unseen Data
print("Test confusion matrix")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [133]:
train_confusion_matrix = pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred, tr_thresholds,
test_fpr,test_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(train_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Test Confusion Matix")
Out[133]:
2.4.2 Applying KNN brute force on TFIDF_SET-2¶
In [135]:
X_train2 = hstack((train_categories_one_hot,train_sub_categories_one_hot,train_state_one_hot,train_grade_one_hot,
train_teacher_one_hot,train_text_tfidf, train_title_tfidf, train_quantity_standar,
train_prev_proj_standar, train_price_standar)).tocsr()
X_test2 = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
test_teacher_one_hot, test_text_tfidf, test_title_tfidf, test_quantity_standar,
test_prev_proj_standar, test_price_standar)).tocsr()
X_cv2 = hstack((cv_categories_one_hot,cv_sub_categories_one_hot,cv_state_one_hot,cv_grade_one_hot,
cv_teacher_one_hot, cv_text_tfidf, cv_title_tfidf, cv_quantity_standar,
cv_prev_proj_standar, cv_price_standar)).tocsr()
print(X_train2.shape, y_train.shape)
print(X_test2.shape, y_test.shape)
print(X_cv2.shape, y_cv.shape)
In [136]:
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import roc_auc_score
"""
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators.
y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of
decisions (as returned by “decision_function” on some classifiers).
For binary y_true, y_score is supposed to be the score of the class with greater label.
"""
train_auc = []
cv_auc = []
K = [1, 5, 10, 15, 21, 31, 41, 51, 101]
for i in tqdm(K):
neigh = KNeighborsClassifier(n_neighbors=i)
neigh.fit(X_train2, y_train)
y_train_pred = batch_predict(neigh, X_train2)
y_cv_pred = batch_predict(neigh, X_cv2)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
train_auc.append(roc_auc_score(y_train,y_train_pred))
cv_auc.append(roc_auc_score(y_cv, y_cv_pred))
plt.plot(K, train_auc, label='Train AUC')
plt.plot(K, cv_auc, label='CV AUC')
plt.scatter(K, train_auc, label='Train AUC points')
plt.scatter(K, cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
Fitting Model to Hyperparameter Curve¶
In [137]:
best_k_2 = 107
# The Highest value of K can neglect the noise and outliers
In [138]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
neigh = KNeighborsClassifier(n_neighbors=best_k_2)
neigh.fit(X_train1, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(neigh, X_train1)
y_test_pred = batch_predict(neigh, X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Confusion Matix¶
In [139]:
print("="*100)
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
In [140]:
train_confusion_matrix = pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred, tr_thresholds,
train_fpr,train_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(train_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Train Confusion Matix")
Out[140]:
Test Data¶
In [ ]:
In [141]:
#This is Future Unseen Data
print("Test confusion matrix for Test Data")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [147]:
test_confusion_matrix = pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred, tr_thresholds,
test_fpr,test_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(test_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Test Confusion Matix")
Out[147]:
In [ ]:
In [ ]:
2.4.3 Applying KNN brute force on AVG W2V_SET-3¶
In [148]:
#train_avg_w2v_vectors
X_train3 = hstack((train_categories_one_hot,train_sub_categories_one_hot,train_state_one_hot,train_grade_one_hot,
train_teacher_one_hot,train_avg_w2v_vectors, train_title_avg_w2v_vectors, train_quantity_standar,
train_prev_proj_standar, train_price_standar)).tocsr()
X_test3 = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
test_teacher_one_hot, test_avg_w2v_vectors, test_title_avg_w2v_vectors, test_quantity_standar,
test_prev_proj_standar, test_price_standar)).tocsr()
X_cv3 = hstack((cv_categories_one_hot,cv_sub_categories_one_hot,cv_state_one_hot,cv_grade_one_hot,
cv_teacher_one_hot, cv_avg_w2v_vectors, cv_title_avg_w2v_vectors, cv_quantity_standar,
cv_prev_proj_standar, cv_price_standar)).tocsr()
print(X_train3.shape, y_train.shape)
print(X_test3.shape, y_test.shape)
print(X_cv3.shape, y_cv.shape)
In [149]:
#train_avg_w2v_vectors
#test_avg_w2v_vectors
train_auc = []
cv_auc = []
K = [1, 5, 10, 15, 21, 31, 41, 51, 101]
for i in tqdm(K):
neigh = KNeighborsClassifier(n_neighbors=i)
neigh.fit(X_train3, y_train)
y_train_pred = batch_predict(neigh, X_train3)
y_cv_pred = batch_predict(neigh, X_cv3)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
train_auc.append(roc_auc_score(y_train,y_train_pred))
cv_auc.append(roc_auc_score(y_cv, y_cv_pred))
plt.plot(K, train_auc, label='Train AUC')
plt.plot(K, cv_auc, label='CV AUC')
plt.scatter(K, train_auc, label='Train AUC points')
plt.scatter(K, cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
Fitting Model to HyperParameter Curve¶
In [151]:
best_k_3 = 121
# The Highest value of K can neglect the noise and outliers
In [152]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
neigh = KNeighborsClassifier(n_neighbors=best_k_3)
neigh.fit(X_train3, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(neigh, X_train3)
y_test_pred = batch_predict(neigh, X_test3)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Confusion Matrix¶
In [153]:
print("="*100)
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
In [154]:
train_confusion_matrix = pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred, tr_thresholds,
train_fpr,train_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(train_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Train Confusion Matix")
Out[154]:
Confusion Matrix for Test Data¶
In [155]:
#This is Future Unseen Data
print("Test confusion matrix for Test Data")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [156]:
test_confusion_matrix = pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred, tr_thresholds,
test_fpr,test_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(test_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Test Confusion Matix")
Out[156]:
2.4.4 Applying KNN brute force on TFIDF W2V_SET-4¶
In [160]:
X_train4 = hstack((train_categories_one_hot,train_sub_categories_one_hot,train_state_one_hot,train_grade_one_hot,
train_teacher_one_hot, train_essay_tfidf_w2v_vectors, train_title_tfidf_w2v_vectors, train_quantity_standar,
train_prev_proj_standar, train_price_standar)).tocsr()
X_test4 = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
test_teacher_one_hot, test_essay_tfidf_w2v_vectors, test_title_tfidf_w2v_vectors, test_quantity_standar,
test_prev_proj_standar, test_price_standar)).tocsr()
X_cv4 = hstack((cv_categories_one_hot,cv_sub_categories_one_hot,cv_state_one_hot,cv_grade_one_hot,
cv_teacher_one_hot, cv_essay_tfidf_w2v_vectors, cv_title_tfidf_w2v_vectors, cv_quantity_standar,
cv_prev_proj_standar, cv_price_standar)).tocsr()
print(X_train4.shape, y_train.shape)
print(X_test4.shape, y_test.shape)
print(X_cv4.shape, y_cv.shape)
In [157]:
#train_essay_tfidf_w2v_vectors
#test_essay_tfidf_w2v_vectors
train_auc = []
cv_auc = []
K = [1, 5, 10, 15, 21, 31, 41, 51, 81, 91, 101]
for i in tqdm(K):
neigh = KNeighborsClassifier(n_neighbors=i)
neigh.fit(X_train4, y_train)
y_train_pred = batch_predict(neigh, X_train4)
y_cv_pred = batch_predict(neigh, X_cv4)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
train_auc.append(roc_auc_score(y_train,y_train_pred))
cv_auc.append(roc_auc_score(y_cv, y_cv_pred))
plt.plot(K, train_auc, label='Train AUC')
plt.plot(K, cv_auc, label='CV AUC')
plt.scatter(K, train_auc, label='Train AUC points')
plt.scatter(K, cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
Fitting Model to HyperParameter Curve¶
In [158]:
best_k_4 = 121
# The Highest value of K can neglect the noise and outliers
In [163]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
neigh = KNeighborsClassifier(n_neighbors=best_k_4)
neigh.fit(X_train1, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(neigh, X_train1)
y_test_pred = batch_predict(neigh, X_test1)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
In [ ]:
Confusion Matrix¶
In [164]:
print("="*100)
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
In [165]:
train_confusion_matrix = pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred, tr_thresholds,
train_fpr,train_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(train_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Train Confusion Matix")
Out[165]:
Confusion Matix for Test Data¶
In [166]:
#This is Future Unseen Data
print("Test confusion matrix for Test Data")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [167]:
test_confusion_matrix = pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred, tr_thresholds,
test_fpr,test_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(test_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Test Confusion Matix")
Out[167]:
In [ ]:
2.5 Feature selection with `SelectKBest`
In [168]:
X_train2 = hstack((train_categories_one_hot,train_sub_categories_one_hot,train_state_one_hot,train_grade_one_hot,
train_teacher_one_hot,train_text_tfidf, train_title_tfidf, train_quantity_standar,
train_prev_proj_standar, train_price_standar)).tocsr()
X_test2 = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
test_teacher_one_hot, test_text_tfidf, test_title_tfidf, test_quantity_standar,
test_prev_proj_standar, test_price_standar)).tocsr()
X_cv2 = hstack((cv_categories_one_hot,cv_sub_categories_one_hot,cv_state_one_hot,cv_grade_one_hot,
cv_teacher_one_hot, cv_text_tfidf, cv_title_tfidf, cv_quantity_standar,
cv_prev_proj_standar, cv_price_standar)).tocsr()
print(X_train2.shape, y_train.shape)
print(X_test2.shape, y_test.shape)
print(X_cv2.shape, y_cv.shape)
In [199]:
##ValueError: Input X must be non-negative.
##https://stackoverflow.com/questions/25792012/feature-selection-using-scikit-learn
#from sklearn.datasets import load_digits
#X_train2, y_test = load_digits(return_X_y=True)
#y_test1 = y_test.iloc[0]
#print(type(y_train1))
#print(y_train1)
print(X_train2.shape, y_train.shape)
In [169]:
#https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
##ValueError: Input X must be non-negative.
##https://stackoverflow.com/questions/25792012/feature-selection-using-scikit-learn
X_train2_new = SelectKBest(f_classif, k=2000).fit_transform(X_train2, y_train)
X_test2_new = SelectKBest(f_classif, k=2000).fit_transform(X_test2, y_test)
X_cv2_new = SelectKBest(f_classif, k=2000).fit_transform(X_cv2, y_cv)
In [170]:
#train_essay_tfidf_w2v_vectors
#test_essay_tfidf_w2v_vectors
train_auc = []
cv_auc = []
K = [1, 5, 10, 15, 21, 31, 41, 51, 81, 91, 101]
for i in tqdm(K):
neigh = KNeighborsClassifier(n_neighbors=i)
neigh.fit(X_train2_new, y_train)
y_train_pred = batch_predict(neigh, X_train2_new)
y_cv_pred = batch_predict(neigh, X_cv2_new)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
train_auc.append(roc_auc_score(y_train,y_train_pred))
cv_auc.append(roc_auc_score(y_cv, y_cv_pred))
plt.plot(K, train_auc, label='Train AUC')
plt.plot(K, cv_auc, label='CV AUC')
plt.scatter(K, train_auc, label='Train AUC points')
plt.scatter(K, cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
Fitting to HyperParameter¶
In [171]:
best_K_5 = 131
In [172]:
neigh = KNeighborsClassifier(n_neighbors=91)
neigh.fit(X_train2_new, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(neigh, X_train2_new)
y_test_pred = batch_predict(neigh, X_test2_new)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
Confusion Matrix¶
In [173]:
print("="*100)
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
In [174]:
train_confusion_matrix = pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred, tr_thresholds,
train_fpr,train_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(train_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Train Confusion Matix")
Out[174]:
Confusion Matirx on Test Data¶
In [175]:
#This is Future Unseen Data
print("Test confusion matrix for Test Data")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
In [176]:
test_confusion_matrix = pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred, tr_thresholds,
test_fpr,test_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(test_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Test Confusion Matix")
Out[176]:
KNN on Balanced BOW¶
In [188]:
##https://stackoverflow.com/questions/25792012/feature-selection-using-scikit-learn
X_train1_new = SelectKBest(f_classif, k=2000).fit_transform(X_train1, y_train)
X_test1_new = SelectKBest(f_classif, k=2000).fit_transform(X_test1, y_test)
X_cv1_new = SelectKBest(f_classif, k=2000).fit_transform(X_cv1, y_cv)
In [ ]:
In [189]:
#train_avg_w2v_vectors
#test_avg_w2v_vectors
train_auc = []
cv_auc = []
K = [1, 5, 10, 15, 21, 31, 41, 51, 101]
for i in tqdm(K):
neigh = KNeighborsClassifier(n_neighbors=i)
neigh.fit(X_train1_new, y_train)
y_train_pred = batch_predict(neigh, X_train1_new)
y_cv_pred = batch_predict(neigh, X_cv1_new)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
train_auc.append(roc_auc_score(y_train,y_train_pred))
cv_auc.append(roc_auc_score(y_cv, y_cv_pred))
plt.plot(K, train_auc, label='Train AUC')
plt.plot(K, cv_auc, label='CV AUC')
plt.scatter(K, train_auc, label='Train AUC points')
plt.scatter(K, cv_auc, label='CV AUC points')
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [190]:
neigh = KNeighborsClassifier(n_neighbors=91)
neigh.fit(X_train2_new, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs
y_train_pred = batch_predict(neigh, X_train2_new)
y_test_pred = batch_predict(neigh, X_test2_new)
train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()
In [191]:
print("="*100)
print("Train confusion matrix")
print(confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_fpr)))
train_confusion_matrix = pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred, tr_thresholds,
train_fpr,train_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(train_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Train Confusion Matix")
Out[191]:
In [192]:
#This is Future Unseen Data
print("Test confusion matrix for Test Data")
print(confusion_matrix(y_test, predict(y_test_pred, tr_thresholds, test_fpr, test_fpr)))
test_confusion_matrix = pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred, tr_thresholds,
test_fpr,test_fpr)), range(2),range(2))
sea.set(font_scale=1.4)
sea.heatmap(test_confusion_matrix, annot = True, annot_kws={"size":16}, fmt = 'd')
plt.xlabel("Predicted Value")
plt.ylabel("True Value")
plt.title("Test Confusion Matix")
Out[192]:
3. Conclusions
In [177]:
# Please compare all your models using Prettytable library
#how to use pretty table http://zetcode.com/python/prettytable/
from prettytable import PrettyTable
tb = PrettyTable()
tb.field_names= ("Vectorizer", "Model", "HyperParameter", "AUC")
tb.add_row(["BOW", "Auto", 97, 63])
tb.add_row(["Tf-Idf", "Auto", 107, 65])
tb.add_row(["AVG-W2v", "Auto", 121, 64])
tb.add_row(["Tf-Idf W2v", "Auto", 121, 64])
tb.add_row(["Tf-Idf KBest", "Auto", 91, 57])
print(tb.get_string(titles = "KNN - Observations"))
#print(tb)
In [ ]: